Navigation of tubular networks

ABSTRACT

Methods and apparatuses provide improved navigation through tubular networks such as lung airways by providing improved estimation of location and orientation information of a medical instrument (e.g., an endoscope) within the tubular network. Various input data such as image data, EM data, and robot data are used by different algorithms to estimate the state of the medical instrument, and the state information is used to locate a specific site within a tubular network and/or to determine navigation information for what positions/orientations the medical instrument should travel through to arrive at the specific site. Probability distributions together with confidence values are generated corresponding to different algorithms are used to determine the medical instrument&#39;s estimated state.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/669,258, filed Aug. 4, 2017, which is a continuation of U.S.application Ser. No. 15/268,238, filed Sep. 16, 2016, now U.S. Pat. No.9,727,963, which claims the benefit of and priority to U.S. ProvisionalApplication No. 62/220,770 filed Sep. 18, 2015, entitled “Navigation ofTubular Networks,” the entire disclosure of each of which isincorporated herein by reference.

BACKGROUND 1. Field of Art

This description generally relates to surgical robotics, andparticularly to navigation of a medical instrument within a tubularnetwork of a patient's body.

2. Description of the Related Art

Bronchoscopy is a medical procedure that allows a physician to examinethe inside conditions of a patient's lung airways, such as bronchi andbronchioles. The lung airways carry air from the trachea, or windpipe,to the lungs. During the medical procedure, a thin, flexible tubulartool, known as a bronchoscope, may be inserted into the patient's mouthand passed down the patient's throat into his/her lung airways, andpatients are generally anesthetized in order to relax their throats andlung cavities for surgical examinations and operations during themedical procedure.

A conventional bronchoscope typically includes a light source and asmall camera that allows a physician to inspect a patient's windpipe andairways, and a rigid tube may be used in conjunction with thebronchoscope for surgical purposes, e.g., when there is a significantamount of bleeding in the lungs of the patient or when a large objectobstructs the throat of the patient. When the rigid tube is used, thepatient is often anesthetized. Coincident with the rise of otheradvanced medical devices, the use of robotic bronchoscopes areincreasingly becoming a reality. Robotic bronchoscopes providetremendous advantages in navigation through tubular networks. They areeasy to use and allow therapy and biopsies to be administeredconveniently even during the bronchoscopy stage.

Apart from mechanical devices or platforms, e.g., robotic bronchoscopesdescribed above, various methods and software models may be used to helpwith the surgical operations. As an example, a computerized tomography(CT) scan of the patient's lungs is often performed during pre-operationof a surgical examination. Data from the CT scan may be used to generatea three dimensional (3D) model of airways of the patient's lungs, andthe generated 3D model enables a physician to access a visual referencethat may be useful during the operative procedure of the surgicalexamination.

However, previous techniques for navigation of tubular networks stillhave challenges, even when employing medical devices (e.g., roboticbronchoscopes) and when using existing methods (e.g., performing CTscans and generating 3D models). As one example, motion estimation of amedical device (e.g., a bronchoscope tool) inside a patient's body maynot be accurate based on location and orientation change of the device,and as a result the device's position may not be accurately or correctlylocalized inside the patient's body in real time. Inaccurate locationinformation for such an instrument may provide misleading information tothe physician that uses the 3D model as a visual reference duringmedical operation procedures.

Thus, there is a need for improved techniques for navigating through anetwork of tubular structures.

SUMMARY

The methods and apparatus disclosed herein provide improved navigationthrough tubular networks such as lung airways by providing improvedestimation of location and orientation information of a medicalinstrument like a flexible or rigid elongated medical instrument (e.g.,an endoscope) within the tubular network.

As one example, the apparatus is a robotic endoscopic tool to acquire“raw” location and orientation information (collectively, input data) ofa desired anatomical site or of the endoscopic tool within the tubularnetwork. The endoscopic tool includes a flexible tip and an instrumentdevice manipulator (IDM) coupled to the endoscopic tool. Devices such asan electromagnetic sensor (EM sensor), an imaging device (e.g., opticalsensor), and a robotic control system controlling the medical instrumentare coupled to the instrument tip to collect the input data as theendoscopic tool enters and navigates through the tubular network. TheIDM is used to control movement and position of different roboticcomponents (e.g., the endoscopic tool) of the surgical robotic system. Aprocessor is coupled to the endoscopic tool to receive the input data todetermine moment-by-moment movements and location and orientationinformation of the medical instrument (e.g., a instrument tip) withinthe tubular network.

The processor is instructed by a navigation configuration system to usethe input data to estimate the state of the medical instrument, whichmay include information such as position, orientation, relative andabsolute depth, branch selection, etc. The processor may be furtherinstructed to use the estimated state to locate a specific site within atubular network and/or to determine navigation information for whatpositions/orientations the medical instrument should travel through toarrive at the specific site, which may be referred to as the output dataor navigation data.

The navigation configuration system further includes multiple algorithmmodules employing various navigation algorithms for providing theestimated state and navigation data. Example algorithms used includeEM-based algorithms, image-based algorithms, and robot-based algorithms.The estimated state and navigation data generated after employing thesevarious algorithms makes use of any one or more of the EM-based inputdata, image-based input data, and robot-based input data.

In some embodiments, probability distributions together with confidencevalues are generated by the algorithm modules, which are used todetermine the medical instrument's estimated state. The “probability” ofthe “probability distribution”, as used herein, refers to a likelihoodof an estimation or identification of location and/or orientation of themedical instrument being correct. For example, different probabilitiesmay be calculated indicating the relative likelihood that the medicalinstrument is in one of several different possible airways within thelung. In contrast, the “confidence value, as used herein, reflects ameasure of confidence in the estimation of the state provided by one ofthe algorithms. For example, relatively close to the airway opening, aparticular algorithm may have a high confidence in its estimations ofmedical instrument position and orientation; but further into the bottomof the lung the medical instrument travels, that confidence value maydrop. Generally, the confidence value is based on one or more “external”factors relating to the process by which a result is determined, whereasprobability is a relative measure that arises when trying to determinepossible results from a single algorithm. The algorithms, probabilities,and confidence values may be variously combined to arrive at theestimated state and navigation data.

In one embodiment, before executing an actual surgical operation on apatient, a sequence of pre-operative steps employing the improvednavigation of surgical instruments (e.g., endoscopic) within a tubularnetwork of the patient may be taken. Initially, a CT scan of the tubularnetwork is obtained to generate a 3D model of the tubular network. Atarget area (e.g., a lesion to biopsy) within the tubular network isselected and a corresponding path for a surgical instrument to travelthrough the tubular network to reach the target area is automaticallyplanned and displayed to a user (e.g., a physician responsible for thesurgical operation). After the path is determined, a virtual endoscopicmay be applied to travel through the tubular network to arrive at thetarget area. In the actual surgical operation, the CT scan, thegenerated 3D model as well as other input data (e.g., image data, EMdata, robot data collected over the duration of the surgery) is combinedand repeatedly analyzed during the surgery via the surgicalconfiguration system to provide an estimation of the real-time movementinformation and location/orientation information of the surgicalinstrument (e.g., the endoscope) within the tubular network along withnavigation information, which allows for more convenient operations bythe physician.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A shows an example surgical robotic system, according to oneembodiment.

FIGS. 1B-1F show various perspective views of a robotic platform coupledto the surgical robotic system shown in FIG. 1A, according to oneembodiment.

FIG. 2 shows an example command console for the example surgical roboticsystem, according to one embodiment.

FIG. 3A shows an isometric view of an example independent drivemechanism of the instrument device manipulator (IDM) shown in FIG. 1A,according to one embodiment.

FIG. 3B shows a conceptual diagram that shows how forces may be measuredby a strain gauge of the independent drive mechanism shown in FIG. 3A,according to one embodiment.

FIG. 4A shows a top view of an example endoscope, according to oneembodiment.

FIG. 4B shows an example endoscope tip of the endoscope shown in FIG.4A, according to one embodiment.

FIG. 5 shows an example schematic setup of an EM tracking systemincluded in a surgical robotic system, according to one embodiment.

FIGS. 6A-6B show an example anatomical lumen and an example 3D model ofthe anatomical lumen, according to one embodiment.

FIG. 7 shows a computer-generated 3D model representing an anatomicalspace, according to one embodiment.

FIGS. 8A-8D show example graphs illustrating on-the-fly registration ofan EM system to a 3D model of a path through a tubular network,according to one embodiment.

FIGS. 8E-8F show effect of an example registration of the EM system to a3D model of a branched tubular network, according to one embodiment.

FIG. 9A shows a high-level overview of an example block diagram of anavigation configuration system, according to one embodiment.

FIG. 9B shows an example block diagram of the navigation module shown inFIG. 9A, according to one embodiment.

FIG. 9C shows an example block diagram of the estimated state data storeincluded in the state estimator, according to one embodiment.

FIG. 10A shows an example block diagram of the EM registration module,according to one embodiment.

FIG. 10B shows an example block diagram of the branch selection module,according to one embodiment.

FIG. 10C shows an example block diagram of the motion estimation module,according to one embodiment.

FIG. 10D shows an example block diagram of the object detection module,according to one example.

FIG. 10E shows an example block diagram of the object mapping module,according to one embodiment.

FIG. 10F shows an example block diagram of the topological reasoningmodule, according to one embodiment.

FIG. 11A shows two example identified objects superimposed on an imageof a bronchial network along with a link connecting centers of the twoobjects, according to one embodiment.

FIG. 11B shows a matching between airway lumens in an actual image of areal bronchial network and a 3D model of the network, according to onembodiment.

FIG. 12 shows an example process of generating a probabilitydistribution over multiple values of an estimated state by the stateestimator, according to one embodiment.

FIG. 13 shows an example of how an error can be corrected withnavigation through a tubular network of bronchial tubes by thenavigation configuration system, according to one embodiment.

FIG. 14A shows an example pre-operative sequence of steps of a methodfor navigating the instrument tip to a particular site within thetubular network, according to one embodiment.

FIG. 14B shows an example user interface for navigation of a surgicalinstrument through a tubular network, according to one embodiment.

FIG. 14C shows an example user interface for tool alignment and controlduring an endoscopic procedure, according to one embodiment.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the described system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

DETAILED DESCRIPTION OF DRAWINGS I. Surgical Robotic System

FIG. 1A shows an example surgical robotic system 100, according to oneembodiment. The surgical robotic system 100 includes a base 101 coupledto one or more robotic arms, e.g., robotic arm 102. The base 101 iscommunicatively coupled to a command console, which is further describedwith reference to FIG. 2 in Section II. COMMAND CONSOLE. The base 101can be positioned such that the robotic arm 102 has access to perform asurgical procedure on a patient, while a user such as a physician maycontrol the surgical robotic system 100 from the comfort of the commandconsole. In some embodiments, the base 101 may be coupled to a surgicaloperating table or bed for supporting the patient. Though not shown inFIG. 1 for purposes of clarity, the base 101 may include subsystems suchas control electronics, pneumatics, power sources, optical sources, andthe like. The robotic arm 102 includes multiple arm segments 110 coupledat joints 111, which provides the robotic arm 102 multiple degrees offreedom, e.g., seven degrees of freedom corresponding to seven armsegments. The base 101 may contain a source of power 112, pneumaticpressure 113, and control and sensor electronics 114—includingcomponents such as a central processing unit, data bus, controlcircuitry, and memory—and related actuators such as motors to move therobotic arm 102. The electronics 114 in the base 101 may also processand transmit control signals communicated from the command console.

In some embodiments, the base 101 includes wheels 115 to transport thesurgical robotic system 100. Mobility of the surgical robotic system 100helps accommodate space constraints in a surgical operating room as wellas facilitate appropriate positioning and movement of surgicalequipment. Further, the mobility allows the robotic arms 102 to beconfigured such that the robotic arms 102 do not interfere with thepatient, physician, anesthesiologist, or any other equipment. Duringprocedures, a user may control the robotic arms 102 using controldevices such as the command console.

In some embodiments, the robotic arm 102 includes set up joints that usea combination of brakes and counter-balances to maintain a position ofthe robotic arm 102. The counter-balances may include gas springs orcoil springs. The brakes, e.g., fail safe brakes, may be includemechanical and/or electrical components. Further, the robotic arms 102may be gravity-assisted passive support type robotic arms.

Each robotic arm 102 may be coupled to an instrument device manipulator(IDM) 117 using a mechanism changer interface (MCI) 116. The IDM 117 canbe removed and replaced with a different type of IDM, for example, afirst type of IDM manipulates an endoscope, while a second type of IDMmanipulates a laparoscope. The MCI 116 includes connectors to transferpneumatic pressure, electrical power, electrical signals, and opticalsignals from the robotic arm 102 to the IDM 117. The MCI 116 can be aset screw or base plate connector. The IDM 117 manipulates surgicalinstruments such as the endoscope 118 using techniques including directdrive, harmonic drive, geared drives, belts and pulleys, magneticdrives, and the like. The MCI 116 is interchangeable based on the typeof IDM 117 and can be customized for a certain type of surgicalprocedure. The robotic 102 arm can include a joint level torque sensingand a wrist at a distal end, such as the KUKA AG® LBR5 robotic arm.

The endoscope 118 is a tubular and flexible surgical instrument that isinserted into the anatomy of a patient to capture images of the anatomy(e.g., body tissue). In particular, the endoscope 118 includes one ormore imaging devices (e.g., cameras or other types of optical sensors)that capture the images. The imaging devices may include one or moreoptical components such as an optical fiber, fiber array, or lens. Theoptical components move along with the tip of the endoscope 118 suchthat movement of the tip of the endoscope 118 results in changes to theimages captured by the imaging devices. The endoscope 118 is furtherdescribed with reference to FIGS. 3A-4B in Section IV. Endoscope.

Robotic arms 102 of the surgical robotic system 100 manipulate theendoscope 118 using elongate movement members. The elongate movementmembers may include pull wires, also referred to as pull or push wires,cables, fibers, or flexible shafts. For example, the robotic arms 102actuate multiple pull wires coupled to the endoscope 118 to deflect thetip of the endoscope 118. The pull wires may include both metallic andnon-metallic materials such as stainless steel, Kevlar, tungsten, carbonfiber, and the like. The endoscope 118 may exhibit nonlinear behavior inresponse to forces applied by the elongate movement members. Thenonlinear behavior may be based on stiffness and compressibility of theendoscope 118, as well as variability in slack or stiffness betweendifferent elongate movement members.

FIGS. 1B-1F show various perspective views of the surgical roboticsystem 100 coupled to a robotic platform 150 (or surgical bed),according to various embodiments. Specifically, FIG. 1B shows a sideview of the surgical robotic system 100 with the robotic arms 102manipulating the endoscopic 118 to insert the endoscopic inside apatient's body, and the patient is lying on the robotic platform 150.FIG. 1C shows a top view of the surgical robotic system 100 and therobotic platform 150, and the endoscopic 118 manipulated by the roboticarms is inserted inside the patient's body. FIG. 1D shows a perspectiveview of the surgical robotic system 100 and the robotic platform 150,and the endoscopic 118 is controlled to be positioned horizontallyparallel with the robotic platform. FIG. 1E shows another perspectiveview of the surgical robotic system 100 and the robotic platform 150,and the endoscopic 118 is controlled to be positioned relativelyperpendicular to the robotic platform. In more detail, in FIG. 1E, theangle between the horizontal surface of the robotic platform 150 and theendoscopic 118 is 75 degree. FIG. 1F shows the perspective view of thesurgical robotic system 100 and the robotic platform 150 shown in FIG.1E, and in more detail, the angle between the endoscopic 118 and thevirtual line 160 connecting one end 180 of the endoscopic and therobotic arm 102 that is positioned relatively farther away from therobotic platform is 90 degree.

II. Command Console

FIG. 2 shows an example command console 200 for the example surgicalrobotic system 100, according to one embodiment. The command console 200includes a console base 201, display modules 202, e.g., monitors, andcontrol modules, e.g., a keyboard 203 and joystick 204. In someembodiments, one or more of the command console 200 functionality may beintegrated into a base 101 of the surgical robotic system 100 or anothersystem communicatively coupled to the surgical robotic system 100. Auser 205, e.g., a physician, remotely controls the surgical roboticsystem 100 from an ergonomic position using the command console 200.

The console base 201 may include a central processing unit, a memoryunit, a data bus, and associated data communication ports that areresponsible for interpreting and processing signals such as cameraimagery and tracking sensor data, e.g., from the endoscope 118 shown inFIG. 1. In some embodiments, both the console base 201 and the base 101perform signal processing for load-balancing. The console base 201 mayalso process commands and instructions provided by the user 205 throughthe control modules 203 and 204. In addition to the keyboard 203 andjoystick 204 shown in FIG. 2, the control modules may include otherdevices, for example, computer mice, trackpads, trackballs, controlpads, video game controllers, and sensors (e.g., motion sensors orcameras) that capture hand gestures and finger gestures.

The user 205 can control a surgical instrument such as the endoscope 118using the command console 200 in a velocity mode or position controlmode. In velocity mode, the user 205 directly controls pitch and yawmotion of a distal end of the endoscope 118 based on direct manualcontrol using the control modules. For example, movement on the joystick204 may be mapped to yaw and pitch movement in the distal end of theendoscope 118. The joystick 204 can provide haptic feedback to the user205. For example, the joystick 204 vibrates to indicate that theendoscope 118 cannot further translate or rotate in a certain direction.The command console 200 can also provide visual feedback (e.g., pop-upmessages) and/or audio feedback (e.g., beeping) to indicate that theendoscope 118 has reached maximum translation or rotation.

In position control mode, the command console 200 uses athree-dimensional (3D) map of a patient and pre-determined computermodels of the patient to control a surgical instrument, e.g., theendoscope 118. The command console 200 provides control signals torobotic arms 102 of the surgical robotic system 100 to manipulate theendoscope 118 to a target location. Due to the reliance on the 3D map,position control mode requires accurate mapping of the anatomy of thepatient.

In some embodiments, users 205 can manually manipulate robotic arms 102of the surgical robotic system 100 without using the command console200. During setup in a surgical operating room, the users 205 may movethe robotic arms 102, endoscopes 118, and other surgical equipment toaccess a patient. The surgical robotic system 100 may rely on forcefeedback and inertia control from the users 205 to determine appropriateconfiguration of the robotic arms 102 and equipment.

The display modules 202 may include electronic monitors, virtual realityviewing devices, e.g., goggles or glasses, and/or other means of displaydevices. In some embodiments, the display modules 202 are integratedwith the control modules, for example, as a tablet device with atouchscreen. Further, the user 205 can both view data and input commandsto the surgical robotic system 100 using the integrated display modules202 and control modules.

The display modules 202 can display 3D images using a stereoscopicdevice, e.g., a visor or goggle. The 3D images provide an “endo view”(i.e., endoscopic view), which is a computer 3D model illustrating theanatomy of a patient. The “endo view” provides a virtual environment ofthe patient's interior and an expected location of an endoscope 118inside the patient. A user 205 compares the “endo view” model to actualimages captured by a camera to help mentally orient and confirm that theendoscope 118 is in the correct—or approximately correct—location withinthe patient. The “endo view” provides information about anatomicalstructures, e.g., the shape of an intestine or colon of the patient,around the distal end of the endoscope 118. The display modules 202 cansimultaneously display the 3D model and computerized tomography (CT)scans of the anatomy the around distal end of the endoscope 118.Further, the display modules 202 may overlay the already determinednavigation paths of the endoscope 118 on the 3D model and CT scans.

In some embodiments, a model of the endoscope 118 is displayed with the3D models to help indicate a status of a surgical procedure. Forexample, the CT scans identify a lesion in the anatomy where a biopsymay be necessary. During operation, the display modules 202 may show areference image captured by the endoscope 118 corresponding to thecurrent location of the endoscope 118. The display modules 202 mayautomatically display different views of the model of the endoscope 118depending on user settings and a particular surgical procedure. Forexample, the display modules 202 show an overhead fluoroscopic view ofthe endoscope 118 during a navigation step as the endoscope 118approaches an operative region of a patient.

III. Instrument Device Manipulator

FIG. 3A shows an isometric view of an example independent drivemechanism of the IDM 117 shown in FIG. 1, according to one embodiment.The independent drive mechanism can tighten or loosen the pull wires321, 322, 323, and 324 (e.g., independently from each other) of anendoscope by rotating the output shafts 305, 306, 307, and 308 of theIDM 117, respectively. Just as the output shafts 305, 306, 307, and 308transfer force down pull wires 321, 322, 323, and 324, respectively,through angular motion, the pull wires 321, 322, 323, and 324 transferforce back to the output shafts. The IDM 117 and/or the surgical roboticsystem 100 can measure the transferred force using a sensor, e.g., astrain gauge further described below.

FIG. 3B shows a conceptual diagram that shows how forces may be measuredby a strain gauge 334 of the independent drive mechanism shown in FIG.3A, according to one embodiment. A force 331 may direct away from theoutput shaft 305 coupled to the motor mount 333 of the motor 337.Accordingly, the force 331 results in horizontal displacement of themotor mount 333. Further, the strain gauge 334 horizontally coupled tothe motor mount 333 experiences strain in the direction of the force331. The strain may be measured as a ratio of the horizontaldisplacement of the tip 335 of strain gauge 334 to the overallhorizontal width 336 of the strain gauge 334.

In some embodiments, the IDM 117 includes additional sensors, e.g.,inclinometers or accelerometers, to determine an orientation of the IDM117. Based on measurements from the additional sensors and/or the straingauge 334, the surgical robotic system 100 can calibrate readings fromthe strain gauge 334 to account for gravitational load effects. Forexample, if the IDM 117 is oriented on a horizontal side of the IDM 117,the weight of certain components of the IDM 117 may cause a strain onthe motor mount 333. Accordingly, without accounting for gravitationalload effects, the strain gauge 334 may measure strain that did notresult from strain on the output shafts.

IV. Endoscope

FIG. 4A shows a top view of an example endoscope 118, according to oneembodiment. The endoscope 118 includes a leader 415 tubular componentnested or partially nested inside and longitudinally-aligned with asheath 411 tubular component. The sheath 411 includes a proximal sheathsection 412 and distal sheath section 413. The leader 415 has a smallerouter diameter than the sheath 411 and includes a proximal leadersection 416 and distal leader section 417. The sheath base 414 and theleader base 418 actuate the distal sheath section 413 and the distalleader section 417, respectively, for example, based on control signalsfrom a user of a surgical robotic system 100. The sheath base 414 andthe leader base 418 are, e.g., part of the IDM 117 shown in FIG. 1.

Both the sheath base 414 and the leader base 418 include drivemechanisms (e.g., the independent drive mechanism further described withreference to FIG. 3A-B in Section III. Instrument Device Manipulator) tocontrol pull wires coupled to the sheath 411 and leader 415. Forexample, the sheath base 414 generates tensile loads on pull wirescoupled to the sheath 411 to deflect the distal sheath section 413.Similarly, the leader base 418 generates tensile loads on pull wirescoupled to the leader 415 to deflect the distal leader section 417. Boththe sheath base 414 and leader base 418 may also include couplings forthe routing of pneumatic pressure, electrical power, electrical signals,or optical signals from IDMs to the sheath 411 and leader 414,respectively. A pull wire may include a steel coil pipe along the lengthof the pull wire within the sheath 411 or the leader 415, whichtransfers axial compression back to the origin of the load, e.g., thesheath base 414 or the leader base 418, respectively.

The endoscope 118 can navigate the anatomy of a patient with ease due tothe multiple degrees of freedom provided by pull wires coupled to thesheath 411 and the leader 415. For example, four or more pull wires maybe used in either the sheath 411 and/or the leader 415, providing eightor more degrees of freedom. In other embodiments, up to three pull wiresmay be used, providing up to six degrees of freedom. The sheath 411 andleader 415 may be rotated up to 360 degrees along a longitudinal axis406, providing more degrees of motion. The combination of rotationalangles and multiple degrees of freedom provides a user of the surgicalrobotic system 100 with a user friendly and instinctive control of theendoscope 118.

FIG. 4B illustrates an example endoscope tip 430 of the endoscope 118shown in FIG. 4A, according to one embodiment. In FIG. 4B, the endoscopetip 430 includes an imaging device 431 (e.g., a camera), illuminationsources 432, and ends of EM coils 434. The illumination sources 432provide light to illuminate an interior portion of an anatomical space.The provided light allows the imaging device 431 to record images ofthat space, which can then be transmitted to a computer system such ascommand console 200 for processing as described herein. Electromagneticcoils 434 located on the tip 430 may be used with an electromagnetictracking system to detect the position and orientation of the endoscopetip 430 while it is disposed within an anatomical system. In someembodiments, the coils may be angled to provide sensitivity toelectromagnetic fields along different axes, giving the ability tomeasure a full 6 degrees of freedom: three positional and three angular.In other embodiments, only a single coil may be disposed within theendoscope tip 430, with its axis oriented along the endoscope shaft ofthe endoscope 118; due to the rotational symmetry of such a system, itis insensitive to roll about its axis, so only 5 degrees of freedom maybe detected in such a case. The endoscope tip 430 further comprises aworking channel 436 through which surgical instruments, such as biopsyneedles, may be inserted along the endoscope shaft, allowing access tothe area near the endoscope tip.

V. Registration Transform of EM System to 3D Model

V. A. Schematic Setup of an EM Tracking System

FIG. 5 shows an example schematic setup of an EM tracking system 505included in a surgical robotic system 500, according to one embodiment.In FIG. 5, multiple robot components (e.g., window field generator,reference sensors as described below) are included in the EM trackingsystem 505. The robotic surgical system 500 includes a surgical bed 511to hold a patient's body. Beneath the bed 511 is the window fieldgenerator (WFG) 512 configured to sequentially activate a set of EMcoils (e.g., the EM coils 434 shown in FIG. 4B). The WFG 512 generatesan alternating current (AC) magnetic field over a wide volume; forexample, in some cases it may create an AC field in a volume of about0.5×0.5×0.5 m.

Additional fields may be applied by further field generators to aid intracking instruments within the body. For example, a planar fieldgenerator (PFG) may be attached to a system arm adjacent to the patientand oriented to provide an EM field at an angle. Reference sensors 513may be placed on the patient's body to provide local EM fields tofurther increase tracking accuracy. Each of the reference sensors 513may be attached by cables 514 to a command module 515. The cables 514are connected to the command module 515 through interface units 516which handle communications with their respective devices as well asproviding power. The interface unit 516 is coupled to a system controlunit (SCU) 517 which acts as an overall interface controller for thevarious entities mentioned above. The SCU 517 also drives the fieldgenerators (e.g., WFG 512), as well as collecting sensor data from theinterface units 516, from which it calculates the position andorientation of sensors within the body. The SCU 517 may be coupled to apersonal computer (PC) 518 to allow user access and control.

The command module 515 is also connected to the various IDMs 519 coupledto the surgical robotic system 500 as described herein. The IDMs 519 aretypically coupled to a single surgical robotic system (e.g., thesurgical robotic system 500) and are used to control and receive datafrom their respective connected robotic components; for example, roboticendoscope tools or robotic arms. As described above, as an example, theIDMs 519 are coupled to an endoscopic tool (not shown here) of thesurgical robotic system 500.

The command module 515 receives data passed from the endoscopic tool.The type of received data depends on the corresponding type ofinstrument attached. For example, example received data includes sensordata (e.g., image data, EM data), robot data (e.g., endoscopic and IDMphysical motion data), control data, and/or video data. To better handlevideo data, a field-programmable gate array (FPGA) 520 may be configuredto handle image processing. Comparing data obtained from the varioussensors, devices, and field generators allows the SCU 517 to preciselytrack the movements of different components of the surgical roboticsystem 500, and for example, positions and orientations of thesecomponents.

In order to track a sensor through the patient's anatomy, the EMtracking system 505 may require a process known as “registration,” wherethe system finds the geometric transformation that aligns a singleobject between different coordinate systems. For instance, a specificanatomical site on a patient has two different representations in the 3Dmodel coordinates and in the EM sensor coordinates. To be able toestablish consistency and common language between these two differentcoordinate systems, the EM tracking system 505 needs to find thetransformation that links these two representations, i.e., registration.For example, the position of the EM tracker relative to the position ofthe EM field generator may be mapped to a 3D coordinate system toisolate a location in a corresponding 3D model.

V. B. 3D Model Representation

FIGS. 6A-6B show an example anatomical lumen 600 and an example 3D model620 of the anatomical lumen, according to one embodiment. Morespecifically, FIGS. 6A-6B illustrate relationship of centerlinecoordinates, diameter measurements and anatomical spaces between theactual anatomical lumen 600 and its 3D model 620. In FIG. 6A, theanatomical lumen 600 is roughly tracked longitudinally by centerlinecoordinates 601, 602, 603, 604, 605, and 606 where each centerlinecoordinate roughly approximates the center of the tomographic slice ofthe lumen. The centerline coordinates are connected and visualized by acenterline 607. The volume of the lumen can be further visualized bymeasuring the diameter of the lumen at each centerline coordinate, e.g.,coordinates 608, 609, 610, 611, 612, and 613 represent the measurementsof the lumen 600 corresponding to coordinates 601, 602, 603, 604, 605,and 606.

FIG. 6B shows the example 3D model 620 of the anatomical lumen 600 shownin FIG. 6A, according to one embodiment. In FIG. 6B, the anatomicallumen 600 is visualized in 3D space by first locating the centerlinecoordinates 601, 602, 603, 604, 605, and 606 in 3D space based on thecenterline 607. As one example, at each centerline coordinate, the lumendiameter is visualized as a 2D circular space (e.g., the 2D circularspace 630) with diameters 608, 609, 610, 611, 612, and 613. Byconnecting those 2D circular spaces to form a 3D space, the anatomicallumen 600 is approximated and visualized as the 3D model 620. Moreaccurate approximations may be determined by increasing the resolutionof the centerline coordinates and measurements, i.e., increasing thedensity of centerline coordinates and measurements for a given lumen orsubsection. Centerline coordinates may also include markers to indicatepoint of interest for the physician, including lesions.

In some embodiments, a pre-operative software package is also used toanalyze and derive a navigation path based on the generated 3D model ofthe anatomical space. For example, the software package may derive ashortest navigation path to a single lesion (marked by a centerlinecoordinate) or to several lesions. This navigation path may be presentedto the operator intra-operatively either in two-dimensions orthree-dimensions depending on the operator's preference.

FIG. 7 shows a computer-generated 3D model 700 representing ananatomical space, according to one embodiment. As discussed above inFIGS. 6A-6B, the 3D model 700 may be generated using centerline 701 thatwas obtained by reviewing CT scans that were generated preoperatively.In some embodiments, computer software may be able to map the navigationpath 702 within the tubular network to access an operative site 703within the 3D model 700. In some embodiments, the operative site 703 maybe linked to an individual centerline coordinate 704, which allows acomputer algorithm to topologically search the centerline coordinates ofthe 3D model 700 for the optimum path 702 within the tubular network.

In some embodiments, the distal end of the endoscopic tool within thepatient's anatomy is tracked, and the tracked location of the endoscopictool within the patient's anatomy is mapped and placed within a computermodel, which enhances the navigational capabilities of the tubularnetwork. In order to track the distal working end of the endoscopictool, i.e., location and orientation of the working end, a number ofapproaches may be employed, either individually or in combination.

In a sensor-based approach to localization, a sensor, such as anelectromagnetic (EM) tracker, may be coupled to the distal working endof the endoscopic tool to provide a real-time indication of theprogression of the endoscopic tool. In EM-based tracking, an EM tracker,embedded in the endoscopic tool, measures the variation in theelectromagnetic field created by one or more EM transmitters. Thetransmitters (or field generators), may be placed close to the patient(e.g., as part of the surgical bed) to create a low intensity magneticfield. This induces small-currents in sensor coils in the EM tracker,which are correlated to the distance and angle between the sensor andthe generator. The electrical signal may then be digitized by aninterface unit (on-chip or PCB) and sent via cables/wiring back to thesystem cart and then to the command module. The data may then beprocessed to interpret the current data and calculate the preciselocation and orientation of the sensor relative to the transmitters.Multiple sensors may be used at different locations in the endoscopictool, for instance in leader and sheath in order to calculate theindividual positions of those components. Accordingly, based on readingsfrom an artificially-generated EM field, the EM tracker may detectchanges in field strength as it moves through the patient's anatomy.

V. C. On-the-Fly Electromagnetic Registration

FIGS. 8A-8D show example graphs 810-840 illustrating on-the-flyregistration of an EM system to a 3D model of a path through a tubularnetwork, according to one embodiment. The navigation configurationsystem described herein allows for on-the-fly registration of the EMcoordinates to the 3D model coordinates without the need for independentregistration prior to an endoscopic procedure. In more detail, FIG. 8Ashows that the coordinate systems of the EM tracking system and the 3Dmodel are initially not registered to each other, and the graph 810 inFIG. 8A shows the registered (or expected) location of an endoscope tip801 moving along a planned navigation path 802 through a branchedtubular network (not shown here), and the registered location of theinstrument tip 801 as well as the planned path 802 are derived from the3D model. The actual position of the tip is repeatedly measured by theEM tracking system 505, resulting in multiple measured location datapoints 803 based on EM data. As shown in FIG. 8A, the data points 803derived from EM tracking are initially located far from the registeredlocation of the endoscope tip 801 expected from the 3D model, reflectingthe lack of registration between the EM coordinates and the 3D modelcoordinates. There may be several reasons for this, for example, even ifthe endoscope tip is being moved relatively smoothly through the tubularnetwork, there may still be some visible scatter in the EM measurement,due to breathing movement of the lungs of the patient.

The points on the 3D model may also be determined and adjusted based oncorrelation between the 3D model itself, image data received fromoptical sensors (e.g., cameras) and robot data from robot commands. The3D transformation between these points and collected EM data points willdetermine the initial registration of the EM coordinate system to the 3Dmodel coordinate system.

FIG. 8B shows a graph 820 at a later temporal stage compared with thegraph 810, according to one embodiment. More specifically, the graph 820shows the expected location of the endoscope tip 801 expected from the3D model has been moved farther along the preplanned navigation path802, as illustrated by the shift from the original expected position ofthe instrument tip 801 shown in FIG. 8A along the path to the positionshown in FIG. 8B. During the EM tracking between generation of the graph810 and generation of graph 820, additional data points 803 have beenrecorded by the EM tracking system but the registration has not yet beenupdated based on the newly collected EM data. As a result, the datapoints 803 in FIG. 8B are clustered along a visible path 814, but thatpath differs in location and orientation from the planned navigationpath 802 the endoscope tip is being directed by the operator to travelalong. Eventually, once sufficient data (e.g., EM data) is accumulated,compared with using only the 3D model or only the EM data, a relativelymore accurate estimate can be derived from the transform needed toregister the EM coordinates to those of the 3D model. The determinationof sufficient data may be made by threshold criteria such as total dataaccumulated or number of changes of direction. For example, in abranched tubular network such as a bronchial tube network, it may bejudged that sufficient data have been accumulated after arriving at twobranch points.

FIG. 8C shows a graph 830 shortly after the navigation configurationsystem has accumulated a sufficient amount of data to estimate theregistration transform from EM to 3D model coordinates, according to oneembodiment. The data points 803 in FIG. 8C have now shifted from theirprevious position as shown in FIG. 8B as a result of the registrationtransform. As shown in FIG. 8C, the data points 803 derived from EM datais now falling along the planned navigation path 802 derived from the 3Dmodel, and each data point among the data points 803 is now reflecting ameasurement of the expected position of endoscope tip 801 in thecoordinate system of the 3D model. In some embodiments, as further dataare collected, the registration transform may be updated to increaseaccuracy. In some cases, the data used to determine the registrationtransformation may be a subset of data chosen by a moving window, sothat the registration may change over time, which gives the ability toaccount for changes in the relative coordinates of the EM and 3Dmodels—for example, due to movement of the patient.

FIG. 8D shows an example graph 840 in which the expected location of theendoscope tip 801 has reached the end of the planned navigation path802, arriving at the target location in the tubular network, accordingto one embodiment. As shown in FIG. 8D, the recorded EM data points 803is now generally tracks along the planned navigation path 802, whichrepresents the tracking of the endoscope tip throughout the procedure.Each data point reflects a transformed location due to the updatedregistration of the EM tracking system to the 3D model.

In some embodiments, each of the graphs shown in FIGS. 8A-8D can beshown sequentially on a display visible to a user as the endoscope tipis advanced in the tubular network. In some embodiments, the processorcan be configured with instructions from the navigation configurationsystem such that the model shown on the display remains substantiallyfixed when the measured data points are registered to the display byshifting of the measured path shown on the display in order to allow theuser to maintain a fixed frame of reference and to remain visuallyoriented on the model and on the planned path shown on the display.

FIGS. 8E-8F show effect of an example registration of the EM system to a3D model of a branched tubular network, according to one embodiment. InFIGS. 8E-8F, 3D graphs showing electromagnetic tracking data 852 and amodel of a patient's bronchial system 854 are illustrated without (shownin FIG. 8E) and with (shown in FIG. 8F) a registration transform. InFIG. 8E, without registration, tracking data 860 have a shape thatcorresponds to a path through the bronchial system 854, but that shapeis subjected to an arbitrary offset and rotation. In FIG. 8F, byapplying the registration, the tracking data 852 are shifted androtated, so that they correspond to a path through the bronchial system854.

V. D. Mathematical Analysis of Registration Transform

In terms of detailed analysis (e.g., mathematical analysis) and methodsof the registration, in some embodiments, a registration matrix can beused to perform the registration between the EM tracking system and the3D model, and as one example, the matrix may represent a translation androtation in 6 dimensions. In alternative embodiments, a rotationalmatrix and a translation vector can be used for performing theregistration.

${M_{1}(\theta)} = \begin{pmatrix}1 & 0 & 0 \\0 & {\cos\;\theta} & {\sin\;\theta} \\0 & {{- \sin}\;\theta} & {\cos\;\theta}\end{pmatrix}$ ${M_{2}(\varphi)} = \begin{pmatrix}{\cos\;\varphi} & 0 & {{- \sin}\;\varphi} \\0 & 1 & 0 \\{\sin\;\varphi} & 0 & {\cos\;\varphi}\end{pmatrix}$ ${M_{3}(\psi)} = \begin{pmatrix}{\cos\;\psi} & {\sin\;\psi} & 0 \\{{- \sin}\;\psi} & {\cos\;\psi} & 0 \\0 & 0 & 1\end{pmatrix}$

From a perspective view of mathematical reasoning, as one example,applying a registration transform involves a shift from one coordinatesystem (x,y,z) to a new coordinate system (x′,y′,z′) that may in generalhave its axes rotated to a different 3D orientation as well as havingits origin shifted an arbitrary amount in each dimension. For example, arotation to an azimuthal angle of radians θ may be expressed by thematrix M₁, a rotation to an inclination angle of φ radians may beexpressed by the matrix M₂ etc., and further rotational matrices may bewritten as the product of rotation matrices. Similarly, a translationvector of (Δx Δy Δz) may be chosen to represent a translation of theorigin in the x, y and z axes by Δx, Δy, and Δz respectively.

The registration transform may be determined by such methods as singularvalue decomposition on a cross correlation matrix between measured EMpositions and estimated positions in the 3D model. The transformationmatrix components may then be extracted from the decomposition, e.g., byidentifying the appropriate principle components. An error signal mayalso be generated from the residuals of the determined transform, andthe size of the error signal may be used to determine a level ofconfidence in the position. As further data are taken and theregistration transform is determined more accurately, this error signalmay decrease, indicating an increasing confidence in positions estimatedin this manner.

VI. Navigation Configuration System

VI. A. High-Level Overview of Navigation Configuration System

FIGS. 9A-9C show example block diagrams of a navigation configurationsystem 900, according to one embodiment. More specifically, FIG. 9Ashows a high-level overview of an example block diagram of thenavigation configuration system 900, according to one embodiment. InFIG. 9A, the navigation configuration system 900 includes multiple inputdata stores, a navigation module 905 that receives various types ofinput data from the multiple input data stores, and an output navigationdata store 990 that receives output navigation data from the navigationmodule. The block diagram of the navigation configuration system 900shown in FIG. 9A is merely one example, and in alternative embodimentsnot shown, the navigation configuration system 900 can include differentand/or addition entities. Likewise, functions performed by variousentities of the system 900 may differ according to differentembodiments.

The input data, as used herein, refers to raw data gathered from and/orprocessed by input devices (e.g., command module, optical sensor, EMsensor, IDM) for generating estimated state information for theendoscope as well as output navigation data. The multiple input datastores 910-940 include an image data store 910, an EM data store 920, arobot data store 930, and a 3D model data store 940. Each type of theinput data stores stores the name-indicated type of data for access anduse by the navigation module 905. Image data may include one or moreimage frames captured by the imaging device at the instrument tip, aswell as information such as frame rates or timestamps that allow adetermination of the time elapsed between pairs of frames. Robot dataincludes data related to physical movement of the medical instrument orpart of the medical instrument (e.g., the instrument tip or sheath)within the tubular network. Example robot data includes command datainstructing the instrument tip to reach a specific anatomical siteand/or change its orientation (e.g., with a specific pitch, roll, yaw,insertion, and retraction for one or both of a leader and a sheath)within the tubular network, insertion data representing insertionmovement of the part of the medical instrument (e.g., the instrument tipor sheath), IDM data, and mechanical data representing mechanicalmovement of an elongate member of the medical instrument, for examplemotion of one or more pull wires, tendons or shafts of the endoscopethat drive the actual movement of the medial instrument within thetubular network. EM data is collected by EM sensors and/or the EMtracking system as described above. 3D model data is derived from 2D CTscans as described above.

The output navigation data store 990 receives and stores outputnavigation data provided by the navigation module 905. Output navigationdata indicates information to assist in directing the medical instrumentthrough the tubular network to arrive at a particular destination withinthe tubular network, and is based on estimated state information for themedical instrument at each instant time, the estimated state informationincluding the location and orientation of the medical instrument withinthe tubular network. In one embodiment, as the medical instrument movesinside the tubular network, the output navigation data indicatingupdates of movement and location/orientation information of the medicalinstrument is provided in real time, which better assists its navigationthrough the tubular network.

To determine the output navigation data, the navigation module 905locates (or determines) the estimated state of the medical instrumentwithin a tubular network. As shown in FIG. 9A, the navigation module 905further includes various algorithm modules, such as an EM-basedalgorithm module 950, an image-based algorithm module 960, and arobot-based algorithm module 970, that each may consume mainly certaintypes of input data and contribute a different type of data to a stateestimator 980. As illustrated in FIG. 9A, the different kinds of dataoutput by these modules, labeled EM-based data, the image-based data,and the robot-based data, may be generally referred to as “intermediatedata” for sake of explanation. The detailed composition of eachalgorithm module and of the state estimator 980 is more fully describedin FIG. 9B below.

VI. B. Navigation Module

FIG. 9B shows an example block diagram of the navigation module 905shown in FIG. 9A, according to one embodiment. As introduced above, thenavigation module 905 further includes a state estimator 980 as well asmultiple algorithm modules that employ different algorithms fornavigating through a tubular network. For clarity of description, thestate estimator 980 is described first, followed by the description ofthe various modules that exchange data with the state estimator 980.

VI. B. 1 State Estimator

The state estimator 980 included in the navigation module 905 receivesvarious intermediate data and provides the estimated state of theinstrument tip as a function of time, where the estimated stateindicates the estimated location and orientation information of theinstrument tip within the tubular network. The estimated state data arestored in the estimated data store 985 that is included in the stateestimator 980.

FIG. 9C shows an example block diagram of the estimated state data store985 included in the state estimator 980, according to one embodiment.The estimated state data store 985 may include a bifurcation data store1086, a position data store 1087, a depth data store 1088, and anorientation data store 1089, however this particular breakdown of datastorage is merely one example, and in alternative embodiments not shown,different and/or additional data stores can be included in the estimatedstate data store 985.

The various stores introduced above represent estimated state data in avariety of ways. Specifically, bifurcation data refers to the locationof the medical instrument with respect to the set of branches (e.g.,bifurcation, trifurcation or a division into more than three branches)within the tubular network. For example, the bifurcation data can be setof branch choices elected by the instrument as it traverses through thetubular network, based on a larger set of available branches asprovided, for example, by the 3D model which maps the entirety of thetubular network. The bifurcation data can further include information infront of the location of the instrument tip, such as branches(bifurcations) that the instrument tip is near but has not yet traversedthrough, but which may have been detected, for example, based on thetip's current position information relative to the 3D model, or based onimages captured of the upcoming bifurcations.

Position data indicates three-dimensional position of some part of themedical instrument within the tubular network or some part of thetubular network itself. Position data can be in the form of absolutelocations or relative locations relative to, for example, the 3D modelof the tubular network. As one example, position data can include theposition of a specific branch.

Depth data indicates depth information of the instrument tip within thetubular network. Example depth data includes the total insertion(absolute) depth of the medical instrument into the patient as well asthe (relative) depth within an identified branch. Depth data may bedetermined based on position data regarding both the tubular network andmedical instrument.

Orientation data indicates orientation information of the instrumenttip, and may include overall roll, pitch, and yaw in relation to the 3Dmodel as well as pitch, roll, raw within an identified branch.

Turning back to FIG. 9B, the state estimator 980 provides the estimatedstate data back to the algorithm modules for generating more accurateintermediate data, which the state estimator uses to generate improvedand/or updated estimated states, and so on forming a feedback loop. Forexample, as shown in FIG. 9B, the EM-based algorithm module 950 receivesprior EM-based estimated state data (not shown in FIG. 9B), alsoreferred to as data associated with timestamp “t−1.” The state estimator980 uses this data to generate “estimated state data (prior)” that isassociated with timestamp “t−1,” It then provides the data back to theEM-based algorithm module. The “estimated state data (prior)” may bebased on a combination of different types of intermediate data (e.g.,robotic data, image data) that is associated with timestamp “t−1” asgenerated and received from different algorithm modules. Next, theEM-based algorithm module 950 runs its algorithms using the estimatedstate data (prior) to output to the state estimator 980 improved andupdated EM-based estimated state data, which is represented by “EM-basedestimated state data (current)” here and associated with timestamp t.This process continues to repeat for future timestamps as well.

As the state estimator 980 may use several different kinds ofintermediate data to arrive at its estimates of the state of the medicalinstrument within the tubular network, the state estimator 980 isconfigured to account for the various different kinds of errors anduncertainty in both measurement and analysis that each type ofunderlying data (robotic, EM, image) and each type of algorithm modulemight create or carry through into the intermediate data used forconsideration in determining the estimated state. To address these, twoconcepts are discussed, that of a probability distribution and that ofconfidence value.

The “probability” of the “probability distribution”, as used herein,refers to a likelihood of an estimation of a possible location and/ororientation of the medical instrument being correct. For example,different probabilities may be calculated by one of the algorithmmodules indicating the relative likelihood that the medical instrumentis in one of several different possible branches within the tubularnetwork. In one embodiment, the type of probability distribution (e.g.,discrete distribution or continuous distribution) is chosen to matchfeatures of an estimated state (e.g., type of the estimated state, forexample continuous position information vs. discrete branch choice). Asone example, estimated states for identifying which segment the medicalinstrument is in for a trifurcation may be represented by a discreteprobability distribution, and may include three discrete values of 20%,30% and 50% representing chance as being in the location inside each ofthe three branches as determined by one of the algorithm modules. Asanother example, the estimated state may include a roll angle of themedical instrument of 40±5 degrees and a segment depth of the instrumenttip within a branch may be is 4±1 mm, each represented by a Gaussiandistribution which is a type of continuous probability distribution.Different methods can be used to generate the probabilities, which willvary by algorithm module as more fully described below with reference tolater figures.

In contrast, the “confidence value,” as used herein, reflects a measureof confidence in the estimation of the state provided by one of thealgorithms based one or more factors. For the EM-based algorithms,factors such as distortion to EM Field, inaccuracy in EM registration,shift or movement of the patient, and respiration of the patient mayaffect the confidence in estimation of the state. Particularly, theconfidence value in estimation of the state provided by the EM-basedalgorithms may depend on the particular respiration cycle of thepatient, movement of the patient or the EM field generators, and thelocation within the anatomy where the instrument tip locates. For theimage-based algorithms, examples factors that may affect the confidencevalue in estimation of the state include illumination condition for thelocation within the anatomy where the images are captured, presence offluid, tissue, or other obstructions against or in front of the opticalsensor capturing the images, respiration of the patient, condition ofthe tubular network of the patient itself (e.g., lung) such as thegeneral fluid inside the tubular network and occlusion of the tubularnetwork, and specific operating techniques used in, e.g., navigating orimage capturing.

For example one factor may be that a particular algorithm has differinglevels of accuracy at different depths in a patient's lungs, such thatrelatively close to the airway opening, a particular algorithm may havea high confidence in its estimations of medical instrument location andorientation, but the further into the bottom of the lung the medicalinstrument travels that confidence value may drop. Generally, theconfidence value is based on one or more systemic factors relating tothe process by which a result is determined, whereas probability is arelative measure that arises when trying to determine the correct resultfrom multiple possibilities with a single algorithm based on underlyingdata.

As one example, a mathematical equation for calculating results of anestimated state represented by a discrete probability distribution(e.g., branch/segment identification for a trifurcation with threevalues of an estimated state involved) can be as follows:S ₁ =C _(EM) *P _(1,EM) +C _(Image) *P _(1,Image) +C _(Robot) *P_(1,Robot);S ₂ =C _(EM) *P _(2,EM) +C _(Image) *P _(2,Image) +C _(Robot) *P_(2,Robot);S ₃ =C _(EM) *P _(3,EM) +C _(Image) *P _(3,Image) +C _(Robot) *P_(3,Robot).

In the example mathematical equation above, S_(i) (i=1, 2, 3) representspossible example values of an estimated state in a case where 3 possiblesegments are identified or present in the 3D model, C_(EM), C_(Image),and C_(Robot) represents confidence value corresponding to EM-basedalgorithm, image-based algorithm, and robot-based algorithm andP_(i,EM), P_(i,Image), and P_(i,Robot) represent the probabilities forsegment i.

To better illustrate the concepts of probability distributions andconfidence value associated with estimate states, a detailed example isprovided here. In this example, a user is trying to identify segmentwhere a instrument tip is located in a certain trifurcation within acentral airway (the predicted region) of the tubular network, and threealgorithms modules are used including EM-based algorithm, image-basedalgorithm, and robot-based algorithm. In this example, a probabilitydistribution corresponding to the EM-based algorithm may be 20% in thefirst branch, 30% in the second branch, and 50% in the third (last)branch, and the confidence value applied to this EM-based algorithm andthe central airway is 80%. For the same example, a probabilitydistribution corresponding to the image-based algorithm may be 40%, 20%,40% for the first, second, and third branch, and the confidence valueapplied to this image-based algorithm is 30%; while a probabilitydistribution corresponding to the robot-based algorithm may be 10%, 60%,30% for the first, second, and third branch, and the confidence valueapplied to this image-based algorithm is 20%. The difference ofconfidence values applied to the EM-based algorithm and the image-basedalgorithm indicates that the EM-based algorithm may be a better choicefor segment identification in the central airway, compared with theimage-based algorithm. An example mathematical calculation of a finalestimated state can be:

for the first branch: 20%*80%+40%*30%+10%*20%=30%; for the secondbranch: 30%*80%+20%*30%+60%*20%=42%; and for the third branch:50%*80%+40%*30%+30%*20%=58%.

In this example, the output estimated state for the instrument tip canbe the result values (e.g., the resulting 30%, 42% and 58%), orderivative value from these result values such as the determination thatthe instrument tip is in the third branch.

As above the estimated state may be represented in a number of differentways. For example, the estimated state may further include an absolutedepth from airway to location of the tip of the instrument, as well as aset of data representing the set of branches traversed by the instrumentwithin the tubular network, the set being a subset of the entire set ofbranches provided by the 3D model of the patient's lungs, for example.The application of probability distribution and confidence value onestimated states allows improved accuracy of estimation of locationand/or orientation of the instrument itp within the tubular network.

VI. B. 2 Algorithm Modules

A shown in FIG. 9B, the algorithm modules include an EM-based algorithmmodule 950, an image-based algorithm module 960, and a robot-basedalgorithm module 970. The algorithm modules shown in FIG. 9B is merelyone example, and in alternative embodiments, different and/additionalalgorithm modules involving different and/or additional navigationalgorithms can also be included in the navigation module 905.

VI. B. 2. I. EM-Based Algorithm Module

The EM-based algorithm module 950 further includes an EM registrationmodule 952 and a branch selection module 954. The EM registration module952 performs registration of EM coordinates to 3D model coordinates.FIG. 10A shows an example block diagram of the EM registration module952, according to one embodiment. The EM registration module 952receives as input, estimated state data (prior) (e.g., bifurcation data)from the estimated state data store 1086, the EM data from the EM datastore 920, the 3D model data from the 3D model data store 940.

As described above with respect to Section V, based on the receiveddata, the EM registration module 952 performs on-the-fly registration ofthe EM tracking data to the 3D model. After the initial registration isdetermined, the EM registration module 952 continually updates itsestimate of the registration transform based on received data, so as toincrease transform accuracy as well as to compensate for changes to thenavigation configuration system 900, e.g., changes due to movement ofthe patient. The EM registration module 952 outputs registrationtransform data to the registration transform data store 1053. In oneembodiment, the registration transform data reflects the best fitregistration transform, and it can also be sent to the state estimator980, as well as to the branch selection module 954.

FIG. 10B shows an example block diagram of the branch selection module954, according to one embodiment. The branch selection module 954receives as inputs, estimated state data (prior) (e.g., bifurcationdata) from the estimated state data store 985, the EM data from the EMdata store 920, registration transform data from the registrationtransform data store 1053, as well as 3D model data from the 3D modeldata store 940. Based on the received data, the branch selection module954 determines an estimate of the position and orientation of theendoscope tip relative to the 3D model of the tubular network andprovides EM-based estimated state data (current) to the state estimator980. As an example, the EM-based estimated state data may be representedas a probability distribution (e.g., a discrete distribution of 20%, 30%and 50% for three segments of a trifurcation, as described above.)Additionally, when at a bifurcation as indicated by the receivedbifurcation data, the branch selection module 954 may compare the pitchand yaw of the tip to the angles of each branch in the 3D model toestimate which branch has been selected by the user for traversal. Thebranch selection module 954 outputs the EM-based estimated state data(current) to the estimated data store 985.

VI. B. 2. H. Image-Based Algorithm Module

Turning back to FIG. 9B, the image-based algorithm module 960 uses imagedata to determine the estimated state of the instrument within thetubular network. The image-based algorithm module 960 further includesone or more different types of image-based algorithm modules that employdifferent image-based algorithms. As shown in FIG. 9B, one exampleincluding an object-based algorithm module 962 is shown. In alternativeembodiments not shown, other types of image-based algorithms may beemployed and corresponding algorithm modules may be included in theimage-based algorithm module 960.

The object-based algorithm module 962 detects and analyzes objectspresent in the field of view of the image data, such as branch openingsor particles, to determine estimated state. In one embodiment, itincludes an object detection module 963, and object mapping module 964,a topological reasoning module 965, and a motion estimation module 966.In some embodiments, it may or may not be necessary to apply thedifferent modules 963, 964, 965 and 966 in a fixed sequential order, andwhen actually executing a process of object-based algorithm described bythe object-based algorithm module 962, the order of employing eachmodule within the module 962 is a different order than shown in FIG. 9B.

Turning to FIG. 10C, the motion estimation module 963 receives as inputsimage data from the image data store 910, estimated state data (prior)(specifically bifurcation data), from the estimated state data store 985as well as the 3D model data from the 3D model data store 940. Based onthe received image data, the motion estimation module 963 measures amovement of the medical instrument between multiple image frames basedon the received image data. Example techniques used include optical flowand image registration techniques, among others. This measurementdetermines a differential movement, such as forward-backward motion orroll motion, of the instrument tip in its own local frame of reference.This movement can be combined with the prior estimated state input tocalculate a new estimated state. In particular, a forward (or backward)movement can translate into an increase (or decrease) in depth relativeto a prior estimated state. Similarly, a differential roll translatesinto a change in roll angle relative to a prior estimated state. Thesemeasurements allow an estimation of movement through the tubularnetwork. As above, these estimations may be represented as a probabilitydistribution (e.g., a roll angle of the medical instrument of 40±5degrees represented by a Gaussian distribution). The output estimatedstate is stored in the estimated state data store 985.

In one embodiment, in a case where the estimated state and bifurcationdata for a particular instant in time indicate that the instrument tipis at or near a branch point, this movement measurement may include anidentification of an estimated new branch that the instrument tip isestimated to be entering or have entered. For example, if thebifurcation data indicates that the endoscope tip is at a branch point,pitch and yaw movements can be measured to determine changes in pointingangle, and the new estimated angle can be compared with the expectedangles of different branches in the 3D model of the tubular network. Adetermination can then be made of which branch the endoscope is facingtowards when it is moved into a new branch. Estimated state datareflecting each of these estimates of new position, orientation, and/orbranch entry are output to the state estimator 980.

FIG. 10D shows an example block diagram of the object detection module964, according to one example. The object detection module 964 receivesas inputs image data (e.g., image frames), and outputs object data to anobject data store 1063 as well as estimated state data to the estimatedstate data store 985. Object data indicates information about whatobjects were identified, as well as positions, orientations, and sizesof objects represented as probabilities.

Specifically, the object detection module 964 detects, within an image,one or more objects and one or more features of the object(s) that mayindicate branch points in a tubular network, and then determines theirposition, size, and orientation. Objects may be calculated orrepresented in the object detection module 964 as being two-dimensionalshapes, such as circles/ovals/ellipses for detected branch points. Thiscorresponds to the fact that the image data used to capture the objectsare images from the camera on the instrument tip pointed usually alongan axis substantially parallel to the direction of the segment in whichthe instrument is located. As a consequence, objects such as branches inthe tubular network appear as simple shapes such as ellipses in theimages. In one embodiment, in a given image within a tubular network,each branch will typically appear as a dark, approximately ellipticalregion, and these regions may be detected automatically by a processor,using region-detection algorithms such as maximally stable extremalregions (MSER) as objects. These regions may then be fit to define anobject (e.g., ellipse), with appropriate free parameters such as ellipsecenter, major and minor axes, and angle within the image. The rollmeasurement and the identified matching between model lumens and lumensin the image are also output to the state estimator 980, as well astopological reasoning module 966. An example of identified objectssuperimposed on an image of a bronchial network, along with a linkjoining their centers, is described with reference to FIGS. 11A-11B.

In one embodiment, “airway” can also be identified as an object presentin the image data. The object detection module 964 may use lightreflective intensity combined with other techniques to identify airways.

The object detection module 964 may further track detected objectsacross a set of sequential image frames to detect which branch has beenentered from among a set of possible branches in the tubular network.Tracking the relative positions of the objects within the image framesmay be used to determine a local, absolute measurement of roll anglewithin a branched network.

FIG. 10E shows an example block diagram of the object mapping module965, according to one embodiment. The object mapping module 965 receivesas inputs 3D model data from the 3D model data store 940, object data(e.g., detected objects such as shapes representing possible branches inthe tubular network) from the object data store 1063, and estimatedstate data (prior) from the estimated state data store 985.

Based on the received input data, the object mapping module 965 outputsobject mapping data to an object mapping data store 1065 as well asimage-based estimated state data (current) to the estimated state datastore 985. As one example, the object mapping data indicates mappinginformation between physical branches (lumens) shown in image data(based on the detected objects) and virtual branch information generatedby 3D model. The estimated state data (current) generated by module 965includes identification of each branch of the tubular network visiblewithin the image as well as an estimate of the roll of the endoscope tiprelative to the 3D model. As above, the estimated state data (current)can be represented as a probability distribution. The identification ofthe visible lumens may include coordinates in x and y of each identifiedlumen center within the image, for example based on object sizescorrelated with the 3D model virtual image data, as well as anassociation of each identified lumen location with a particular branchof the tubular network.

In some embodiments, since the 3D model is generated prior to theendoscopic procedure, the virtual images of the tubular network may bepre-computed to speed up processing. In alternative embodiments notshown, the tubular network may be represented by a structure such as atree diagram of lumen midlines, with each such midline describing a 3Dpath, so that an expected position of local branch centers as seen fromany arbitrary perspective may be compared to the identified actuallocations of branch centers based on EM data and/or robot data.

FIGS. 11A-11B, show an example object-to-lumen mapping performed by theobject mapping module 965, according to one embodiment. Morespecifically, FIG. 11A shows two example identified objects 1101 and1102 superimposed on an image of a bronchial network 1105 along with alink 1103 connecting centers of the two objects, according to oneembodiment. In the illustrated example, the identified objects 1101 and1102 are ellipse-shaped.

FIG. 11B shows a matching between airway lumens in an actual image 1110of a real bronchial network and a corresponding virtual image 1120 froma 3D model of that same network, according to on embodiment. In theactual image 1110, ellipses are identified corresponding to twodifferent branches, located with identified centers 1111 and 1112,which, in one embodiment, indicates centerline coordinates of thebranches as described above in FIGS. 6A-6B. The 3D model virtual image1120 is a simulated representation of the real bronchial network shownin the actual image 1110, and the estimated centers 1121 and 1122 of theendoscope tip as determined by the state estimator 980 are showncorresponding to the positions of the identified centers 1111 and 1112.

If both images 1110 and 1120 are presented to a user via a userinterface, the 3D model image 1120 may be rotated or translated toincrease the closeness of fit between actual image 1110 and virtualimage 1120, and the amount of roll needed for the rotation ortranslation can be output as a correction to the current estimated state(e.g., roll of the instrument tip).

In one embodiment, the probability applied to a possible estimated stateas generated by the object mapping module 965 is based on the closenessof fit between the identified centers 1111 and 1112 detected in theactual image 1110 and estimated centers 1121 and 1121 in the 3D modelimage 1120, and as one example, the probability of being in the lumenwith identified center 1112 drops as the distance between the estimatedcenter 1122 and identified center 1112 increases.

FIG. 10F shows an example block diagram of the topological reasoningmodule 966, according to one embodiment. The topological reasoningmodule 966 receives as input image data from the 3D model data from the3D model data store 940, object mapping data from the object mappingdata store 1065, and estimated state data (prior) from the estimatedstate data store 985.

Based on the received data, the topological reasoning module 966determines which branch the endoscope tip is facing towards, therebygenerating a prediction of which branch will be entered if the endoscopeis moved forward. As above, the determination may be represented as aprobability distribution. In one embodiment, when the instrument tip ismoving forward, the topological reasoning module 966 determines that anew branch of the tubular network has been entered and identifies whichbranch the tip has moved into. The determination of which branch isbeing faced and which segment is entered may be made, for example, bycomparing the relative sizes and locations of different identifiedobjects (e.g., ellipses). As one example, as a particular lumen branchis entered, a corresponding detected object will grow larger insuccessive image frames, and will also become more centered in thoseframes. If this is behavior is identified for one of the objects, thetopological reasoning module 966 assigns an increasingly largeprobability to a corresponding estimated state as the endoscope tipmoves towards the lumen associated with that object. Other branches areassigned correspondingly lower probabilities, until finally their objectshapes disappear from images entirely. In one embodiment, theprobability of the medical instrument being in those branches dependsonly on the probability that the branches were misidentified by theobject mapping module 964. The output of the topological reasoningmodule 966 is image-based estimated state data representing estimatedprobabilities of being in each of a set of possible branches within thebranched network.

VI. B. 2. III. Robot-Based Algorithm Module

The robot-based algorithm module 970 uses robot data to providerobot-based estimated state data to the state estimator 980. FIG. 9Billustrates that the robot-based algorithm module 970 receives estimatedstate data (prior) and provides estimated state data (current) to stateestimator 980. Robot data includes data related to physical movement ofthe medical instrument or part of the medical instrument (e.g., theinstrument tip) within the tubular network. Example robot data includescommand data instructing the instrument tip to reach a specificanatomical site and/or change its orientation (e.g., with a specificpitch, roll, yaw, insertion, and retraction for one or both of a leaderand a sheath) within the tubular network, insertion data representinginsertion movement of the part of the medical instrument (e.g., theinstrument tip or sheath), IDM data, and mechanical data representingmechanical movement of an elongate member of the medical instrument, forexample motion of one or more pull wires, tendons or shafts of theendoscope that drive the actual movement of the medical instrumentwithin the tubular network.

Although in an ideal system, specifically input pitch, roll, yaw,insertion, and retraction commands given to the IDM to control theinstrument tip would result in exactly-as-input changes in the motion ofthe instrument tip, in practice this is generally not the case. Frictionin the system, nonlinearities in instrument motion, blockages, and othereffects may cause the input motion to vary from the output motion. Assuch, the estimated state data provided by the raw robotic input data isjust that, an estimate as to actual motion. As per the algorithms above,the estimated state data determined from the robotic data may berepresented in a probabilistic manner to represent this uncertainty inactual position information.

VI. C. Probability Distribution Generation

FIG. 12 shows an example process of generating a probabilitydistribution over multiple values to determine an estimated state as maybe performed by any of the individual algorithm modules described above,according to one embodiment. For sake of example, this process isdescribed with respect to the estimated state module 980, but inpractice may be used by any algorithm module.

In FIG. 12, a process of a Bayesian estimation method is shown, and inalternative embodiments not shown, other methods of generating aprobability distribution can also be employed. As shown in FIG. 12, thestate estimator 980 first determines 1201 a prior probabilitydistribution for the estimated state. As this stage, this probabilitydistribution may be an initialized value distribution based, forexample, on user input identifying a starting point near an entry pointto a branched network. Subsequently, the distribution is determined fromits previous output state estimate, adjusted based on the robotic,image, or EM input data.

For example, if the surgical robotic system 100 commands to move theendoscope tip forward a certain distance, e.g., 1 mm, the new or updatedprobability distribution may be estimated as centered 1 mm forwardcompared to the previous probability distribution. More generally, if arobot command is expected to change a variable x by a certain amount,represented by an expected distribution of value changes Q(Ax), the newestimated distribution P′ (x) may be expressed as a convolution of theprevious distribution P(x) with Q(Ax). This new estimated state istreated as a prior probability estimate of the variable for thesubsequent steps.

In the next step 1202, the state estimator 980 receives an estimatedvalue distribution for the estimated state based on one or morealgorithm modules. This value distribution may be represented in variousways, such as an expected value and an error range, or as an explicitdistribution function over values, for example. In any case, the valuedistribution contains, implicitly or explicitly, an expected value ofthe variable and some estimate of a range of error. In some cases, onlythe expected value is transmitted and the error range is determined bythe state estimator 980, using a record of past performance orpre-calibrated values of reliability. In one embodiment, when using anexpected value and an error range, the state estimator 980 may treat theestimated value distribution as a normal distribution with a mean at theexpected value and a standard deviation determined by the error range.In the case of a discrete variable or discrete state like branchidentification, the estimated value distribution will typically compriseone or more probability values assigned to each of one or more branches,such that the total probability sums to one (or 100%). In some cases,some branches may not be assigned with explicit probabilities, and thetotal probability may be less than 1, in which case the remainingprobability can be treated as being uniformly distributed over all otherbranches.

In step 1203, the state estimator 980 generates a posterior probabilitydistribution based on the received estimated value distribution fromstep 1202 and the determined prior probability distribution from step1201. As one example, this posterior probability distribution iscalculated as an application of Bayes' Theorem, which states that for anobservation A, the posterior probability P(x|A) of a variable havingvalue x is given by the equation

${{P\left( {x❘A} \right)} = \frac{{P\left( {A❘x} \right)}{P(x)}}{P(A)}},$where P(x|A) is the probability of observing A given that x is true,P(x) is the prior probability of x being true, and P(A) is the priorprobability of observing A, whether x is true or false.

The state estimator 980 determines each of the quantities P(x), P(A),and P(A|x) from the inputs generated during steps 1201 and 1202, as wellas from a basic model of the set of possible observations. For example,P(A), representing the prior probability of x, may simply be read fromthe value determined in step 1201. Likewise, P(A|x) may be read from thefunction determined in step 1202. P(A), may be set as a constantrepresenting the number of possible values of A. For example, whenestimating a discrete probability distribution for what branch anendoscope tip is in encountering a division into a certain number (e.g.,10) of branches, the probability, P(A), of observing the tip to be inany given branch, may be set as 1/10. Alternatively, a model may beimplemented to weight the possible observations A variably, for example,where branch sizes vary, P(A) may be set proportional to the size ofbranch A divided by the sum of all branch sizes, representing the ideathat a randomly-placed tip is more likely to be found in a large branchthan a small one. For continuous distributions, such as roll angle,depth, or orientation, P(A) will be a probability density function overall possible observations A. For example, if roll angle ranges from 0 to2π radians, P(A) may be set to 1/(2π) for all values of A to representan equally likely probability density of any roll angle being observed.More generally, given P(x) as an estimate for the probability of x, P(A)will be given by the formula P(A)=P(A|x)P(x)+P(A|˜x)P(˜x), where ˜xmeans “not-x.”

In one embodiment, the result of step 1203 is a new probabilitydistribution for values of the variable/state. In one embodiment, wheneach of multiple independent measurements relates to a given variable,the multiple independent measurements may be adjusted sequentially bytaking the output of step 1203 as an input to step 1201, and using eachnew measurement as the estimated value distribution of step 1202. Thisgenerates a loop 1205 over measurements. Alternatively, step 1202 mayincorporate a plurality of independent estimated value distributionwhich may be combined into a single updated estimate in step 1203, usingBayesian conditional updating on multiple measurements.

For example, if differential movement is being measured, the motionestimation module 963 may employ method 1200 with a process of takingthe prior probability distribution of step 1201 as over an amount ofmovement (for example, an expected range of movement based on robotdata), receiving an estimated value distribution in step 1202 overdifferential movement measurements, and generating a posteriorprobability distribution in step 1203 to estimate actual values ofmovement. This output may then be convolved with a prior estimatedvariable values (from a prior estimated state) to generate a posteriorprobability distribution for multiple values of the state. In someembodiments, once all measurements have been incorporated into anupdated probability distribution, this new probability distribution isreported out in step 1204. The process 1200 may be applied in parallelto generate new probability distributions for a plurality of variables(states), such as position, orientation, roll, depth, and/or branch. Theoutputs of step 1204 for each such process may be combined together togenerate a new estimated state (E) 1202, which represents the output ofthe motion estimation module 963.

VI. D. Error Correction

In some embodiments, the navigation module 905 allows estimates ofvariables even when certain input data is not available. For example,prior to registration of the EM system, the output of the branchselection module 954 is ignored by the state estimator 980. Nonetheless,states representing location and/or orientation (e.g., tip insertiondepth) may still be estimated based on available input data like therobot input data. Each time the instrument tip is ordered to move deeperinto the branched network, the estimate state for the tip insertiondepth may be updated based on this estimated movement. Thus, prior toregistration, the tip insertion depth may be estimated based on robotdata, and after registration, the depth may be estimated based on datafrom the branch selection module 954, but also partially based on robotdata, with a weighting function.

There is a possibility of over-determination/over-fitting of thelocation estimate for the instrument based on the many possibleindependent measurements introduced above, which include localmeasurements such as object-based image tracking, optical flow, globalmeasurements such as EM tracking, and robotic input data basedmeasurement. Consequently, the accuracy of estimated position within thebranched network may be greater than the accuracy of estimates madeusing any one of the modules alone. Furthermore, in some embodiments,the navigation configuration system 900 demonstrates the ability torecover from errors by using the measurements from one module tocontradict the measurements from another, allowing the system to “changeits mind” about a previously-made determination. An example of how anerror can be corrected is more fully described below with reference toFIG. 13.

FIG. 13 shows an example of how an error can be corrected withnavigation through a tubular network by the navigation configurationsystem 900, according to one embodiment. In FIG. 13, a simplified modelof a bronchial tree 1300 with estimated states is shown at fourdifferent temporal stages: states 1310, 1320, 1330, and 1340. For eachtemporal stage, an estimated position (1312, 1322, 1332, and 1342,respectively) of an instrument tip used for the navigation isillustrated, and a corresponding actual position (1311, 1321, 1331, and1341, respectively) of the tip is also illustrated. In one embodiment,over time during the four stages, the accuracy of the estimated states(based on the underlying probabilities and confidences) may vary, e.g.,the estimated states may first begin accurate, then becomes inaccurate,and then becomes accurate again.

As shown in FIG. 13, initially, in state 1310, the actual position ofthe tip is 1311, at the upper branch of the bronchial tree 1300, and thenavigation configuration system 900 accurately estimates that locationas estimated position 1312. However, when it enters one of the twobranches near actual position 1311, one or more modules inside thesystem 900 may misidentify the branch based on probabilities provided byone or more of the algorithm modules, causing the system to concludethat the tip is entering the left branch 1345, when it is actuallyentering the right branch 1346. For example, the state estimator 980initially assigns an 80% chance of being in the left branch and a 20%chance of being in the right branch.

However, as the endoscope tip proceeds down the right branch 1346 tostate 1320, the estimated position 1322 will be more and more spatiallydistinct from the actual position 1321. In one embodiment, the branchselection module 954 may thus more and more confidently report that thetip is probably in the right branch 1346 based on the shift in theprobabilities provided by the underlying modules. Accordingly, the stateestimator's 980 aggregate probability estimate may also shift, therebyresulting in an increase in the probability of being in the right branch1346 and a corresponding decreasing the probability of being in the leftbranch 1345.

The system 900 may proceed to a state 1330, in which the tip is at theactual position 1331, arriving at a division into three branches.However, at this point in time state estimator 980 may still beestimating that the most likely state to be at estimated position 1332.

At state 1340, based on the 3D model, the state estimator 980 may beexpecting a certain distance until the next branch division, in thisexample a two-way division, rather than three-way branch division. Underthis circumstance, both the branch selection module 954 and the objectmapping module 964 may strongly estimate that the tip is located in thebranch shown on the right, which further strongly adjusts theprobabilities of being in the left and right branches, leading to analmost certain assignment of the tip location to the correct branch, theright branch 1346 here.

Consequently, at state 1340 the state estimator 980 correctly estimatesthe instrument tip be at estimated position 1342, very close to itsactual location 1341. If the endoscope is along the user's desired path,the system 900 can now proceed to determine which branch of thethree-way branch division is entered next. Alternately, the user canbacktrack to travel down the left lumen of bronchial tree 1300. In someembodiments, this updated estimate is shown to a user on a display, sothat the user can perceive that the previous estimates were in error,and that error has now been corrected.

VII. Pre-Operative Path Planning for Navigation Preparation

Navigating to a particular point in a tubular network of a patient'sbody requires certain steps to be taken pre-operatively in order togenerate the information needed to create the 3D model of the tubularnetwork and to determine a navigation path within it. FIGS. 14A-14C showexample pre-operative steps for preparation of a surgical instrument(e.g., an instrument tip) to navigation through an example tubularnetwork, according to various embodiments.

FIG. 14A shows an example pre-operative sequence of steps of a method1400 for navigating the instrument tip to a particular site within thetubular network, according to one embodiment. Alongside each step of themethod 1400, a corresponding image is shown to illustrate arepresentation of the involved data for planning a path and navigatingthrough the tubular network.

Initially, in step 1405, a CT scan of the tubular network is obtained,and the data from the CT scan provides 3D information about thestructure and connectivity of the tubular network. For example, theimage in step 1405 shows a tomographic slice of a patient's lungs.

In step 1410, a 3D model is generated based on the obtained CT scandata, and the generated 3D model can be used to assign each branch ofthe tubular network with a unique identity, enabling convenientnavigation within the network. For example, the image in step 1410 showsa 3D model of a patient's bronchial network.

In step 1415, a target 1416 is selected, and this target may be, forexample, a lesion to biopsy, or a portion of organ tissue to repairsurgically. In one embodiment, the user is capable of selecting thelocation of the target by interfacing with a computer display that canshow the 3D model, such as by clicking with a mouse or touching atouchscreen. The selected target may then be displayed to the user. Forexample, the target 1416 is marked within the 3D bronchial modelgenerated from step 1410.

In step 1420, a path 1421 is automatically planned from an entry point1422 to the target 1416, and the path 1421 identifies a sequence ofbranches within the network to travel through, so as to reach the target1416. In one embodiment, the tubular network may be tree-like, the path1421 may be uniquely determined by the structure of the tubular network,while in another embodiment, the tubular network may be cyclic, and thepath may be found by an appropriate algorithm such as a shortest-pathalgorithm.

Once the path 1421 has been determined, virtual endoscopy 1425 may beperformed to give the user a preview of the endoscopic procedure. The 3Dmodel generated from step 1410 is used to generate a sequence of 2Dimages as though seen by an endoscope tip while navigating the networkcorresponding to the 3D model. The path 1421 may be shown as a curvethat may be followed to get from the entry point 1422 to the target1416.

Once the virtual endoscope tip has arrived at the target 1416, a virtualtool alignment procedure 1430 may be performed to illustrate to the userhow to manipulate endoscopic tools in order to perform a surgicalprocedure at the target. For example, in the illustration, a virtualendoscopic biopsy needle 1431 is maneuvered by the user in order tobiopsy a lesion 1432 located beneath the surface of a bronchial tube.The lesion location is highlighted so that the user can align the needleto it, and then use the needle to pierce the surface of the bronchialtube and access the lesion underneath. This mimics the steps that willbe taken during the actual surgical procedure, allowing the user topractice before performing surgery.

FIG. 14B shows an example user interface 1434 for navigation of asurgical instrument through a tubular network, according to oneembodiment. The user interface 1434 allows the user to have variousviews of the tubular network during an endoscopic procedure. In additionto a real-time view from the imaging device (e.g., an endoscope camera),the user interface 1434 may show various navigation views to the user.For example, the location of the endoscope tip may be superimposed on atomographic segment of the CT scan 1435, with a marker (e.g., crosshairs1436) showing the location of the instrument. In this case, the CT imageis of a patient's lungs, and the endoscope used is a bronchoscope.

In FIG. 14B, the user interface 1434 also shows a view 1440 representinga 3D model with a highlighted path 1441 and icons showing the endoscopelocation 1442 and the target location 1443. Some additional icons 1444are also shown in the view 1440 for user interface controls enablingfeatures as map pan and zoom.

View 1445 shows a virtual endoscope view from the estimated location ofthe tip that can be compared with a real-time view to confirm that thetip location has been estimated accurately. A path indicator 1446 andlocation/orientation indicator 1447 for aid in navigation is also shownin the view 1446. The upper left shows state information 1448 determinedby the state estimator 980, indicating that the current distance totarget “t1” is 5.4 cm, and that the calculated roll angle is −139degrees.

View 1450 shows a virtual view of the endoscope shaft 1451, displayedfrom a “third-person” viewpoint to the rear of the endoscope tip. Thevirtual view can be generated by assuming that the endoscope shaft liesalong the path already traversed by the tip, and the view of thesurrounding area is generated based on the 3D model in the vicinity ofthe chosen viewpoint. Alternately, the view may be generated from imagestaken while passing through that area earlier in the procedure. Inalternative embodiments, the virtual viewpoint may project ahead of theendoscope tip location, to show the next bifurcation. This view mayfurther highlight the intended path, to inform the user in advance whichway to steer when the endoscope tip reaches the next bifurcation.

FIG. 14C shows an example user interface 1480 for tool alignment andcontrol during an endoscopic procedure, according to one embodiment.View 1455 shows a tomographic view at a later stage in the procedurethan view 1435, in which the endoscope tip is near the target and anendoscopic tool—in this case, a biopsy needle—has been deployed from theendoscope's working channel. The crosshairs 1436 highlights the newposition of the tip. Since the bronchoscope in the image is farther intothe patient's lungs, the tomographic slice has changed to show a deepercross-sectional segment. The icon locating the endoscope tip has beenreplaced with one indicating the location and orientation of the needle.View 1460 shows a map view of the 3D model, zoomed in around the targetlocation 1443 and the tool icon 1461 representing the biopsy needle.Located at target 1443 is a biopsy location 1466 to which a biopsyneedle is to be applied. As shown in this view, the endoscope tip hasbeen navigated to nearly the end of path 1441, to arrive at a locationclose enough to the target location 1443 so that the needle may reachthe biopsy location 1466.

View 1465 shows a virtual view in the vicinity of target location 1443,highlighting a biopsy location 1466 to help the user visualize preciselywhere the needle is to be inserted to collect tissue for biopsy.Additional information, such as target size and distance to target mayoptionally be overlaid on the image.

View 1670 shows a close-up view of a 3D icon 1471 representing a biopsyneedle, superimposed on a tomographic image of the patient's lungs. Theroll of the instrument is indicated by a multicolored pattern 1475 onthe rear of the 3D icon 1471. As seen from state information 1473, thisparticular orientation represents a roll of 0 degrees, and the needle iscurrently 4 mm from the biopsy location 1466. A path 1474 is displayedshowing how the needle should be oriented and moved in order to contactthe biopsy location 1466.

VIII. Machine Configuration for the Navigation Configuration System

More generally, the navigation and tracking technique disclosed hereinmay be performed with an appropriately configured computer system. Aprocessor within the computer system may comprise one or more componentsto process electronic signals, and may comprise one or more of a centralprocessor unit, a video processor, logic circuitry, gate array logic,filed programmable gate array, integrated circuit, or applicationspecific integrated circuit. The computer system includes a centralprocessing unit (CPU, also “processor” and “computer processor” herein),which can be a single core or multi core processor, or a plurality ofprocessors for parallel processing. The CPU can execute a sequence ofmachine-readable instructions, which can be embodied in a program orsoftware. The instructions may be stored in a memory location. Examplesof operations performed by the CPU can include fetch, decode, execute,and writeback. The CPU can be part of a circuit, such as an integratedcircuit. One or more other components of the system can be included inthe circuit. In some cases, the circuit comprises an applicationspecific integrated circuit (ASIC).

The computer system may also include one or more of memory or memorylocations (e.g., random-access memory, read-only memory, flash memory),electronic storage units (e.g., hard disk), communication interfaces(e.g., network adapters) for communicating with one or more othersystems, and peripheral devices, such as caches, other memory, datastorage and/or electronic display adapters. The memory, storage unit,interface, and peripheral devices are in communication with the CPUthrough a communication bus, such as a motherboard.

The storage unit can be a data storage unit (or data repository) forstoring data. The computer system can be operatively coupled to acomputer network (“network”) with the aid of the communicationinterface. The network can be the Internet, an internet and/or extranet,or an intranet and/or extranet that is in communication with theInternet. The network in some cases is a telecommunication and/or datanetwork, and can include can include one or more computer servers. Thestorage unit can store files, such as drivers, libraries and savedprograms. The storage unit can store user data, e.g., user preferencesand user programs. The computer system in some cases can include one ormore additional data storage units that are external to the computersystem, such as located on a remote server that is in communication withthe computer system through an intranet or the Internet.

Methods as described herein can be implemented by way of machine (e.g.,computer processor) executable code stored on an electronic storagelocation of the computer system, such as, for example, on the memory orelectronic storage unit. The machine executable or machine readable codecan be provided in the form of software. During use, the code can beexecuted by the processor. In some cases, the code can be retrieved fromthe storage unit and stored on the memory for ready access by theprocessor. In some situations, the electronic storage unit can beprecluded, and machine-executable instructions are stored on memory.

The code can be pre-compiled and configured for use with a machine havea processer adapted to execute the code, or can be compiled duringruntime. The code can be supplied in a programming language that can beselected to enable the code to execute in a pre-compiled or as-compiledfashion.

Methods and systems of the present disclosure can be implemented by wayof one or more methods. A method can be implemented with a processor asdescribed herein, for example by way of software upon execution by oneor more computer processors.

IX. Additional Considerations

Alternative views and embodiments of the surgical robotics system 1100,the surgical robotics system 1200, and other surgical robotics systemsincluding the above mentioned components are further illustrated anddescribed at least in U.S. Provisional Application No. 62/162,486 filedMay 15, 2015, U.S. Provisional Application No. 62/162,467 filed May 15,2015, U.S. Provisional Application No. 62/193,604 filed Jul. 17, 2015,U.S. Provisional Application No. 62/201,518 filed Aug. 5, 2015, U.S.Provisional Application No. 62/203,530 filed Aug. 11, 2015, and U.S.Provisional Application No. 62/235,394 filed Sep. 30, 2015.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs throughthe disclosed principles herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those skilled in theart, may be made in the arrangement, operation and details of the methodand apparatus disclosed herein without departing from the spirit andscope defined in the appended claims.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context unlessotherwise explicitly stated.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

What is claimed is:
 1. A surgical robotic system comprising: anendoscopic tool having a flexible tip; an optical sensor coupled to theflexible tip; a processor coupled to the optical sensor, the processorconfigured with instructions to: receive a plurality of images from theoptical sensor; identify, within an image of the plurality of images, adivision of a tubular network, the division comprising a plurality ofopenings to a plurality of respective segments of the tubular network;track the detected plurality of openings over a set of sequential imageframes of the plurality of images to determine probabilities forentering each of the plurality of openings; determine a first estimatedstate of the flexible tip within the tubular network based on theidentified division of the tubular network and the probabilities,wherein the first estimated state comprises at least one of a firstestimated position and a first estimated orientation of the flexible tipwithin the tubular network; and show, on a display, an estimatedposition of the flexible tip within a segment of the plurality ofsegments of the tubular network, wherein the estimated position isdetermined based on the first estimated state.
 2. The system of claim 1,wherein the processor is configured to identify the division with theimage by detecting the plurality of openings within the image.
 3. Thesystem of claim 2, wherein processor is configured to detect theplurality of openings based on light reflective intensity within theimage.
 4. The system of claim 2, wherein processor is configured to:access model data from a three dimensional model of the tubular network;and compare the detected plurality of openings with virtual branchinformation from the model data.
 5. The system of claim 4, wherein thethree dimensional model of the tubular network is generated based oncomputerized axial tomography (CT) scans of the tubular network.
 6. Thesystem of claim 4, wherein the processor is configured to compare thedetected plurality of openings with the virtual branch information fromthe model data by comparing the detected plurality of openings with avirtual image of the division, wherein the virtual image is generatedbased on the model data.
 7. The system of claim 6, wherein the processoris configured to: show the image on the display; rotate the virtualimage to match an orientation of the image; and show the virtual imageon the display.
 8. The system of claim 2, wherein the processor isconfigured to access model data comprising a tree diagram representativeof the tubular network and determine the first estimated state based onthe tree diagram.
 9. The system of claim 1, wherein the plurality ofopenings comprise three openings and the division comprises atrifurcation.
 10. The system of claim 1, wherein the plurality of imagesare captured sequentially in time.
 11. The system of claim 10, whereinthe processor is configured to track relative positions of the detectedplurality of openings over a set of sequential image frames of theplurality of images to determine a measurement of roll of the flexibletip.
 12. The system of claim 1, wherein the processor is configured to:determine a second estimate state of the position of the device withinthe tubular network based on a second set of sensor data; and determinethe estimated position of the flexible tip based on the first estimatedstate and the second estimated state.
 13. The system of claim 12,wherein the second set of sensor data comprises data derived from aposition sensor.
 14. The system of claim 12, wherein the second set ofsensor data comprises data regarding physical manipulation of theendoscopic tool.
 15. A non-transitory computer readable storage mediumcomprising computer program instructions that when executed by one ormore processors cause the one or more processors to: receive a pluralityof images from an optical sensor on a flexible tip of an endoscopic toolinserted into a tubular network; identify, within an image of theplurality of images, a division of the tubular network, the divisioncomprising a plurality of openings to a plurality of respective segmentsof the tubular network; track the detected plurality of openings over aset of sequential image frames of the plurality of images to determineprobabilities for entering each of the plurality of openings; determinea first estimated state of the flexible tip within the tubular networkbased on the identified division of the tubular network and theprobabilities, wherein the first estimated state comprises at least oneof a first estimated position and a first estimated orientation of theflexible tip within the tubular network; and show, on a display, anestimated position of the flexible tip within a segment of the pluralityof segments of the tubular network, wherein the estimated position isdetermined based on the first estimated state.
 16. The non-transitorycomputer readable storage medium of claim 15, wherein the instructions,when executed, cause the one or more processors to: determine a secondestimate state of the position of the device within the tubular networkbased on a second set of sensor data; and determine the estimatedposition of the flexible tip based on the first estimated state and thesecond estimated state.
 17. The non-transitory computer readable storagemedium of claim 16, wherein the second set of sensor data comprises dataderived from a position sensor.
 18. A method for navigating a tubularnetwork, the method comprising: receiving a plurality of images from anoptical sensor on a flexible tip of an endoscopic tool inserted into atubular network; identifying, within an image of the plurality ofimages, a division of the tubular network, the division comprising aplurality of openings to a plurality of respective segments of thetubular network; tracking the detected plurality of openings over a setof sequential image frames of the plurality of images to determineprobabilities for entering each of the plurality of openings;determining a first estimated state of the flexible tip within thetubular network based on the identified division of the tubular networkand the probabilities, wherein the first estimated state comprises atleast one of a first estimated position and a first estimatedorientation of the flexible tip within the tubular network; and showing,on a display, an estimated position of the flexible tip within a segmentof the plurality of segments of the tubular network, wherein theestimated position is determined based on the first estimated state. 19.The method of claim 18, further comprising: determining a secondestimate state of the position of the device within the tubular networkbased on a second set of sensor data; and determining the estimatedposition of the flexible tip based on the first estimated state and thesecond estimated state.